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A noise robust convolutional neural network for image classification
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DOI：10.1016/j.rineng.2021.100225.png)
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Abstract
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Convolutional Neural Networks (CNNs) are extensively used for image classification. Noisy images reduce the classification performance of convolutional neural networks and increase the training time of the networks. In this paper, a Noise-Robust Convolutional Neural Network (NR-CNN) is proposed to classify the noisy images without any preprocessing for noise removal and improve the classification performance of noisy images in convolutional neural networks. In the proposed NR-CNN, a noise map layer and an adaptive resize layer are added to the architecture of convolutional neural network. Moreover, the noise problem is considered in different components of NR-CNN such that convolutional layer, pooling layer and loss function of the convolutional neural network are improved for robustness of CNN to noise. The adaptive data augmentation based on noise map are introduced to improve the classification performance of the proposed NR-CNN. Experimental results demonstrate that the proposed NR-CNN improves the noisy image classification and the network training speed.
Keywords:
Convolutional neural network
Noise
Image classification
Adaptive pooling
Adaptive convolution
Adaptive data augmentation
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